Tag
monte-carlo
- Jul 10, 2026 Rejection Sampling and the Price of the Envelope The acceptance rate of a rejection sampler is exactly the reciprocal of its envelope constant, which is why a proposal that looks cheap can make the loop run thousands of trials per variate. scientific computing
- Jun 4, 2026 Seeds, Generator State, and Reproducible Random Streams Why a seed is not enough to reproduce a stochastic result, how generator state advances, and how parallel workers avoid splitting one stream into correlated pieces. scientific computing
- May 22, 2026 Aggregating Task Estimates: Why Summed Ranges Come Out Too Narrow Why adding per-task ranges produces an interval that is too tight for the whole job, how skew and correlation break the diversification argument, and what to compute instead. software engineering
- Apr 7, 2026 Why Monte Carlo Error Falls as the Square Root of the Sample Count The convergence rate follows from the variance of a sum, and the practical consequences are a fixed digit budget per factor of a hundred in work and an error bar that is itself estimated. scientific computing
- Mar 31, 2026 Forecasting Completion Dates from Historical Throughput Resampling the observed rate of finished work to produce a distribution of completion dates, and the assumptions about scope, unit, and independence that decide whether the answer means anything. software engineering
- Jul 25, 2025 Sampling BRDF Lobes and Choosing Between Them How a probability density over outgoing directions turns a reflectance integral into a weighted average, why GGX sampling has a closed form, and how lobe selection probabilities affect variance. computer graphics
- Jun 7, 2023 Antithetic Variates and the Synchronization of Paired Runs Pairing each draw with its mirror cancels the leading fluctuation of an estimator when the integrand is monotone, and the price is a stream whose consumption must stay aligned. scientific computing
- Aug 30, 2022 Inverse-Transform Sampling and the Generalized Inverse of the CDF Why mapping a uniform variate through the quantile function produces the right distribution, what the discrete case changes, and where floating-point resolution destroys the tails. scientific computing